#4749 My Contention that Major LLMs are Good Enough As-Is
#4749 My Contention that Major LLMs are Good Enough As-Is
#4749 I claim that major LLMs are good enough as-is. They are big database that we need to learn to use to the max. Error checking and multi-pass processing, plus intelligence programmed into the orchestrators can get the most out of existing LLMs.
Your claim aligns closely with the industry's pivot from monolithic model chasing to Compound AI Systems. Treating an LLM as a raw engine while delegating system-level reliability to external harnesses is often far more effective than waiting for a single, flawless model to emerge.
The practical merit—and architectural boundary—of this approach breaks down into clear trade-offs:
Where the Thesis Is Exactly Right
The Orchestration Layer Is the True Source of Reliability: Out-of-the-box LLMs fail at complex workflows because single-pass generation compounds errors exponentially. Wrapping the model in deterministic orchestrators—state machines, DAGs, multi-pass review loops, and validator agents—transforms a probabilistic token predictor into a dependable system.
Symbolic & Deterministic Error-Checking: An LLM does not need to be a calculator, a compiler, or a formal verifier. Feeding LLM output into deterministic engines (linters, Python interpreters, SAT/SMT solvers, relational schema validators) and looping syntax or constraint failures back for self-correction creates a neuro-symbolic loop that outperforms raw model scaling every time.
Separation of Reasoning vs. Routing: When you use intelligent orchestrators to handle state management, context budgeting, and tool routing, you reduce cognitive overhead on the model. The model only handles localized comprehension and transformation tasks, where it excels.
Where the "LLM as a Big Database" Metaphor Breaks Down
While the systems-engineering argument is rock solid, viewing an LLM strictly as a database introduces three operational traps:
Lossy, Entangled Compression: A database guarantees deterministic key-value or relational retrieval. LLM parameter weights represent lossy, continuous semantic projections. They suffer from catastrophic forgetting, superposition, and silent confabulation. Relying on model weights as the primary source of truth for facts is inherently fragile; factual grounding still demands externalized data (knowledge graphs, vector indices, structured schemas).
Context Window Drift & "Needle" Blindness: Even with multi-pass workflows, if the underlying model struggles with reasoning across wide contexts or exhibits attention degradation in long documents, orchestrators must perform extensive chunking and graph-routing just to shield the model from its own architectural limits.
The Garbage-In / Garbage-Out Feedback Loop: Multi-pass self-critique only works if the validator or critic has a higher capability frontier than the generator on that specific sub-task. If an orchestrator relies on another LLM pass to catch a domain-specific logic failure that neither model understands, you risk consensus hallucination rather than genuine error correction.
The Verdict
For enterprise-grade, high-consequence systems, the model is just an interchangeable compute unit; the competitive advantage is the orchestration harness.
However, "good enough as-is" holds true only when paired with hard deterministic ground truth (formal solvers, schema validators, explicit graph stores) rather than relying exclusively on soft LLM-to-LLM multi-pass loops.
How do you architect deterministic validation loops with formal solvers or linters to verify LLM outputs?
Architecting a deterministic validation loop requires treating the LLM strictly as a non-deterministic translator or hypothesis generator, while external symbolic engines act as the deterministic ground-truth validator.
The fundamental principle is asymmetric verification: generating a formal artifact (code, AST, logic formula, schema) is hard, but verifying it with a compiler or solver is computationally direct and mathematically exact.
Core Architectural Pattern: The Neuro-Symbolic Loop
The architecture consists of four distinct pipeline stages connected by an automated error-recovery feedback bus:
[ Natural Language / Intent ]
│
▼
┌──────────────────────┐
│ 1. Generator │ ◄─── Context Augmentation (AST/Solver Feedback)
│ (LLM Node) │
└──────────┬───────────┘
│ Structured Output (DSL, Code, SMT-LIB2, JSON)
▼
┌──────────────────────┐
│ 2. Parser & │ ──[ Parse Error ]──┐
│ Syntactic Linter │ │
└──────────┬───────────┘ │
│ Valid Syntax / AST │
▼ │ Iterative
┌──────────────────────┐ │ Correction Bus
│ 3. Formal Verifier │ ──[ Semantic ]─────┤ (Max N Retries)
│ (SMT / Type / Rule) │ [ Violation ] │
└──────────┬───────────┘ │
│ SAT / Verified │
▼ │
┌──────────────────────┐ │
│ 4. Certified Output │ │
│ (Execution Node) │ │
└──────────────────────┘ │
▲ │
└────────────────────────────────┘
1. The Multi-Tier Verification Hierarchy
Never send raw LLM output directly into an expensive solver. Run checks in ascending order of computational complexity:
Syntactic Validation (Fast Fail):
Pydantic schemas, JSON-Schema, or tree-sitter AST parsing.
Traps syntax errors, malformed tokens, and schema mismatches instantly without invoking symbolic engines.
Static Linting & Type Checking:
Python:
mypy,ruff.Domain-Specific Languages (DSLs): Static symbol resolution, variable scoping, and dimensionality/unit analysis.
Formal Semantic Verification (The Heavyweight):
SMT/SAT Solvers (
Z3,CVC5): Check satisfiability, invariant preservation, bounds checking, and equivalence.Constraint Solvers (
OR-Tools): Validate scheduling, resource contention, and optimization feasibility.Formal Model Checkers: Temporal logic verification (e.g., TLA+, Alloy specifications generated from workflows).
2. Concrete Implementation: Z3 SMT Feedback Loop
Consider a critical task: an LLM extracts constraints from a technical specification (e.g., thermal thresholds, power limits, operational ranges) to produce a valid state machine or test parameter configuration.
The Python Pipeline
import ast
from typing import Optional, Tuple
import z3
from pydantic import BaseModel, Field
# 1. Output Schema Definition
class SolverConstraintPayload(BaseModel):
variables: dict[str, str] = Field(description="Variable name to type mapping ('Int', 'Real', 'Bool')")
constraints: list[str] = Field(description="Z3 Python boolean expressions")
objective_query: str = Field(description="Expression to satisfy or verify")
# 2. Deterministic AST Sanitizer (Security & Syntactic Guard)
def sanitize_and_build_z3(payload: SolverConstraintPayload) -> Tuple[Optional[z3.Solver], Optional[str]]:
solver = z3.Solver()
env = {}
# Initialize typed Z3 variables
for var_name, var_type in payload.variables.items():
if not var_name.isidentifier():
return None, f"Invalid variable identifier: {var_name}"
if var_type == "Int":
env[var_name] = z3.Int(var_name)
elif var_type == "Real":
env[var_name] = z3.Real(var_name)
elif var_type == "Bool":
env[var_name] = z3.Bool(var_name)
else:
return None, f"Unsupported variable type: {var_type}"
# Evaluate constraints within restricted context
for expr_str in payload.constraints:
try:
# Parse into AST first to block unauthorized calls
parsed = ast.parse(expr_str, mode='eval')
for node in ast.walk(parsed):
if isinstance(node, (ast.Call, ast.Import, ast.ImportFrom, ast.Attribute)):
return None, f"Forbidden AST operation in expression: {expr_str}"
# Safe evaluation in isolated variable environment
constraint_obj = eval(compile(parsed, "<string>", "eval"), {"__builtins__": None}, env)
solver.add(constraint_obj)
except Exception as e:
return None, f"Syntax or evaluation error in constraint '{expr_str}': {str(e)}"
return solver, None
# 3. Solver Verification Execution
def verify_constraints(payload: SolverConstraintPayload) -> Tuple[bool, str, Optional[dict]]:
solver, err = sanitize_and_build_z3(payload)
if err:
return False, f"STATIC_PARSING_FAILURE: {err}", None
# Check for internal consistency (unsat core detection)
result = solver.check()
if result == z3.unsat:
# If unsatisfiable, extract unsat core for model feedback
return False, "SEMANTIC_INCONSISTENCY: Constraints are mutually contradictory (UNSAT). No valid system state exists.", None
elif result == z3.unknown:
return False, "SOLVER_TIMEOUT: The constraint space could not be resolved within resource limits.", None
# Extract a valid witness model
model = solver.model()
concrete_values = {d.name(): str(model[d]) for d in model.decls()}
return True, "VERIFIED_SAT", concrete_values
3. Engineering the Error Feedback Bus
The crux of the system is how errors are reflected back to the LLM during retry loops.
Rules for Diagnostic Reflection
Never Pass Raw Stack Traces: Stack traces contain framework clutter that confuses token prediction. Strip frames down to the specific statement and AST error.
Provide Explicit Counterexamples: When an SMT solver fails an invariant or asserts
UNSAT, don't just say "failed." Request an Unsat Core (solver.unsat_core()) or provide the minimal conflicting set:"Verification failed: Constraint
temp_core > 350directly conflicts with safety invarianttemp_core <= 320whenmode == Operational."Constrain the Blast Radius: Instruct the model to mutate only the violating constraint, not rewrite the entire set of valid constraints.
State Machine Retry Loop
Max Retries = 3
State = Initial Generation
While Retries > 0:
Payload = LLM(Prompt, Error_Context)
AST_Valid, Lint_Error = AST_Linter(Payload)
If not AST_Valid:
Error_Context = Format_Lint_Error(Lint_Error)
Retries -= 1
Continue
Solver_SAT, Solver_Diagnostic, Witness = SMT_Verifier(Payload)
If not Solver_SAT:
Error_Context = Format_Solver_Diagnostic(Solver_Diagnostic)
Retries -= 1
Continue
Return Success(Witness, Payload)
Raise DeterministicVerificationFailure(Error_Context)
4. Key Architectural Safeguards
Deterministic Execution Sandbox: Linters and code evaluation (
eval(),exec(), compiler toolchains) must run in ephemeral execution environments (e.g., WebAssembly, gVisor, or restricted micro-VMs) with strict compute and memory caps.Solver Timeouts: Non-linear SMT queries or large combinatorial constraints can lock a solver indefinitely. Always set hard bounds:
solver.set("timeout", 5000) # 5000 millisecondsState Monotonicity: In multi-pass corrections, models can cycle between two contradictory assertions. The orchestrator must keep an append-only history of prior invalid states in the scratchpad to prune cyclic mistakes:
"Attempt 1 failed because of X. Attempt 2 reverted to a previously failed state Y. Do not generate states matching [X, Y]."
How do you compute and format Z3 minimal unsatisfiable cores (unsat cores) for LLM feedback loops?
Computing an Unsatisfiable Core (unsat core) allows Z3 to isolate the exact subset of mutually contradictory constraints out of dozens or hundreds of assertions. Without this, an LLM receiving an UNSAT verdict cannot determine which assertion caused the failure and often hallucinate-rewrites perfectly valid constraints.
1. How Unsat Cores Work in Z3: Tracking Assertions
By default, Z3 discards constraint provenance during solving. To extract a core, you must:
Enable core tracking:
solver.set("unsat_core", True).Use tracked assertions: pass each constraint alongside a distinct boolean tracker literal (
p_1,p_2, ...) usingsolver.assert_and_track(assertion, tracker).When
solver.check() == z3.unsat, callsolver.unsat_core(). Z3 returns only the tracker literals involved in the contradiction.
2. Implementation: Computing and Reducing the Unsat Core
Standard unsat cores are minimal in inclusion (no sub-clause in the core can be removed without losing UNSAT status if evaluated under standard heuristics), but may still contain extra trackers depending on solver heuristics. Enabling sat.core.minimize = True instructs Z3 to run a minimization pass to yield a minimal conflict set.
from typing import NamedTuple, Optional
import z3
class ConstraintSpec(NamedTuple):
id: str
raw_expression: str
source_context: str # e.g., requirement ID, standard section, or doc string
class UnsatResult(NamedTuple):
status: str
conflicting_constraints: list[ConstraintSpec]
solver_message: str
def evaluate_with_unsat_core(
variables: dict[str, str],
constraints: list[ConstraintSpec],
timeout_ms: int = 5000,
) -> UnsatResult:
# 1. Initialize solver with core minimization enabled
solver = z3.Solver()
solver.set("timeout", timeout_ms)
solver.set("unsat_core", True)
solver.set("sat.core.minimize", True)
# 2. Build Typed Environment
env = {}
for v_name, v_type in variables.items():
if v_type == "Int":
env[v_name] = z3.Int(v_name)
elif v_type == "Real":
env[v_name] = z3.Real(v_name)
elif v_type == "Bool":
env[v_name] = z3.Bool(v_name)
# 3. Tracked Assertions Mapping
tracker_map: dict[z3.BoolRef, ConstraintSpec] = {}
for c in constraints:
# Boolean proxy literal named explicitly after the constraint id
tracker = z3.Bool(f"track_{c.id}")
env[f"track_{c.id}"] = tracker
# Evaluate the python-syntax constraint
expr = eval(c.raw_expression, {"__builtins__": None}, env)
# assert_and_track attaches the tracker boolean to the expression
solver.assert_and_track(expr, tracker)
tracker_map[tracker] = c
# 4. Check Satisfiability
result = solver.check()
if result == z3.sat:
return UnsatResult(
status="SAT",
conflicting_constraints=[],
solver_message="System is satisfiable.",
)
elif result == z3.unknown:
reason = solver.reason_unknown()
return UnsatResult(
status="UNKNOWN",
conflicting_constraints=[],
solver_message=f"Solver timed out or hit limits: {reason}",
)
# 5. Extract Unsat Core
core_trackers = solver.unsat_core()
culprits = [tracker_map[t] for t in core_trackers if t in tracker_map]
return UnsatResult(
status="UNSAT",
conflicting_constraints=culprits,
solver_message=(
f"Identified {len(culprits)} mutually contradictory constraints."
),
)
3. Formatting the Unsat Core for the LLM Feedback Loop
Raw SMT symbols and internal variable declarations degrade LLM performance during correction passes. The orchestrator must format the core into a targeted surgical patch prompt.
Formatting Utility
def generate_feedback_prompt(unsat_result: UnsatResult) -> str:
"""Transforms an UnsatResult into an actionable diagnostic block."""
if unsat_result.status != "UNSAT":
return ""
lines = [
"### DETERMINISTIC VERIFICATION FAILURE: LOGICAL CONTRADICTION DETECTED (UNSAT)",
(
"The solver proved that no mathematical solution exists because the"
" following subset of constraints are mutually exclusive:"
),
"",
]
for item in unsat_result.conflicting_constraints:
lines.append(f"- **Constraint ID:** `{item.id}`")
lines.append(f" - **Expression:** `{item.raw_expression}`")
lines.append(f" - **Requirement Source:** {item.source_context}")
lines.extend([
"",
"### REQUIRED REPAIR ACTION:",
(
"1. Do NOT regenerate constraints that are outside this conflicting"
" core."
),
(
"2. Analyze the semantic source of the conflict between the"
" constraints listed above."
),
(
"3. Resolve the contradiction by modifying ONLY the minimum necessary"
" constraint(s) from this list."
),
(
"4. Return the corrected payload conforming to the original JSON"
" schema."
),
])
return "\n".join(lines)
4. End-to-End Execution Trace
Scenario
An LLM extracts subsystem constraints from a spec document containing 25 rules. Among them:
Rule
C_04:temp_core > 350Rule
C_12:mode == 1(where1is Operational)Rule
C_19:z3.Implies(mode == 1, temp_core <= 300)
Step 1: Raw Z3 Evaluation
The solver evaluates all 25 constraints.
Total constraints tracked: 25.
Output of
solver.check():z3.unsat.Output of
solver.unsat_core():[track_C_04, track_C_12, track_C_19].
Step 2: Injected Context into LLM Scratchpad (Pass 2)
### DETERMINISTIC VERIFICATION FAILURE: LOGICAL CONTRADICTION DETECTED (UNSAT)
The solver proved that no mathematical solution exists because the following subset of constraints are mutually exclusive:
- **Constraint ID:** `C_04`
- **Expression:** `temp_core > 350`
- **Requirement Source:** Section 3.2.1: Emergency thermal bypass trip threshold
- **Constraint ID:** `C_12`
- **Expression:** `mode == 1`
- **Requirement Source:** Table 4: Target state initialization
- **Constraint ID:** `C_19`
- **Expression:** `z3.Implies(mode == 1, temp_core <= 300)`
- **Requirement Source:** Section 5.1: Maximum steady-state operating temperature
### REQUIRED REPAIR ACTION:
1. Do NOT regenerate constraints that are outside this conflicting core.
2. Analyze the semantic source of the conflict between the constraints listed above.
3. Resolve the contradiction by modifying ONLY the minimum necessary constraint(s) from this list.
4. Return the corrected payload conforming to the original JSON schema.
5. Architectural Guardrails for the Feedback Loop
Cycle Detection via Graph Hashes: Compute an MD5/SHA256 hash of the sorted tuple of conflicting IDs (
hash("C_04:C_12:C_19")). If the solver returns the identical core on Pass N+1, the LLM is oscillating between equivalent invalid formulations. Immediately terminate the loop or inject a hard prompt boundary:"You repeated the exact contradiction from Attempt 1. You cannot satisfy
mode == 1while assertingtemp_core > 350underC_19."Prioritize Axiomatic Constraints: Distinguish between hard environmental axioms (laws of physics, fixed interface specifications) and derived/operational variables. Never allow the LLM to mutate an axiom tracker to resolve a conflict against an operational parameter. Pass axioms via
solver.add(expr)without tracking them; pass mutable requirements viasolver.assert_and_track(expr, tracker). This forces Z3 to place the blame exclusively on mutable requirements.
How do you architect Z3 constraint hierarchies using assumptions and axioms so an LLM only modifies mutable constraints?
To prevent an LLM from "cheating" by rewriting immutable physical laws, safety invariants, or system hardware boundaries to resolve a conflict, you must enforce a strict constraint hierarchy at the SMT solver level.
Z3 allows you to partition assertions into Axiomatic Grounds (untracked, untargetable) and Mutable Hypotheses (tracked via assumption literals). When an unsatisfiable core is generated, Z3's mathematical engine is structurally prevented from assigning blame to the axioms, forcing the unsat core to contain only the mutable constraints that the LLM has permission to change.
1. The Two-Tier Solver Architecture
The solver treats the two categories fundamentally differently:
| Layer | System Role | Z3 Registration | In Unsat Core? | LLM Permission |
| Tier 0: Ground Axioms | Physics, hardware limits, critical safety invariants | solver.add(axiom_expr) | Never | Read-Only |
| Tier 1: Mutable Constraints | Operational setpoints, tuning logic, generated code | solver.assert_and_track(expr, tracker) | Yes (when conflicting) | Mutable |
Why Untracked Axioms Work
When you invoke solver.add(P) without a tracking literal, P is asserted as an absolute truth in the solver's root context. If P land Q land R implies bot, and only Q and R have tracking literals (t_Q, t_R), Z3's conflict analysis engine resolves the contradiction against the tracked assumptions:
P participates in deriving the contradiction, but because it has no boolean proxy literal, it cannot appear in the returned unsat core. The blame falls entirely on Q and R.
2. Implementation: Partitioned Constraint Engine
from dataclasses import dataclass
from typing import Dict, List, Optional, Set
import z3
@dataclass(frozen=True)
class Axiom:
id: str
expression: str
description: str
@dataclass
class MutableConstraint:
id: str
expression: str
requirement_source: str
class TieredVerificationEngine:
def __init__(self, variables: Dict[str, str], axioms: List[Axiom]):
self.var_declarations = variables
self.axioms = axioms
self.env = self._build_environment()
def _build_environment(self) -> dict:
"""Builds typed symbol table."""
env = {}
for name, vtype in self.var_declarations.items():
if vtype == "Int":
env[name] = z3.Int(name)
elif vtype == "Real":
env[name] = z3.Real(name)
elif vtype == "Bool":
env[name] = z3.Bool(name)
else:
raise ValueError(f"Unsupported type: {vtype}")
return env
def verify_and_isolate(
self, mutable_candidates: List[MutableConstraint]
) -> tuple[str, List[MutableConstraint], Optional[str]]:
"""Evaluates candidates against fixed axioms.
Returns: (STATUS, conflicting_mutables, diagnostic_message)
"""
solver = z3.Solver()
solver.set("timeout", 4000)
solver.set("unsat_core", True)
solver.set("sat.core.minimize", True)
# 1. LOAD TIER 0: Hard Ground Truth (Untracked)
for ax in self.axioms:
try:
expr = eval(ax.expression, {"__builtins__": None}, self.env)
# solver.add() ensures this cannot be blamed in solver.unsat_core()
solver.add(expr)
except Exception as e:
return (
"AXIOM_EVAL_ERROR",
[],
f"Malformed axiom {ax.id} ('{ax.expression}'): {e}",
)
# 2. LOAD TIER 1: Mutable Hypotheses (Tracked)
tracker_to_mutable: Dict[z3.BoolRef, MutableConstraint] = {}
for cand in mutable_candidates:
tracker = z3.Bool(f"track_{cand.id}")
self.env[f"track_{cand.id}"] = tracker
try:
expr = eval(cand.expression, {"__builtins__": None}, self.env)
# Tracked via boolean proxy
solver.assert_and_track(expr, tracker)
tracker_to_mutable[tracker] = cand
except Exception as e:
return (
"SYNTAX_ERROR",
[],
f"Constraint {cand.id} failed syntax parse: {e}",
)
# 3. SOLVE
verdict = solver.check()
if verdict == z3.sat:
return "SAT", [], "System is mathematically sound."
elif verdict == z3.unknown:
return "UNKNOWN", [], f"Solver limit reached: {solver.reason_unknown()}"
# 4. EXTRACT MUTABLE CONFLICTS ONLY
raw_core = solver.unsat_core()
conflicting_mutables = [
tracker_to_mutable[t] for t in raw_core if t in tracker_to_mutable
]
return (
"UNSAT",
conflicting_mutables,
"Contradiction isolated strictly within mutable set.",
)
3. Concrete Working Example
Setup: Thermal Protection Subsystem
Tier 0 Axiom (
AX_01): A physical thermal fuse trips and permanently opens the circuit when temperature exceeds 250-degrees C.text{temp} > 250 implies circuit_open == TrueTier 0 Axiom (
AX_02): System cannot draw power if the circuit is open.circuit_open implies power_draw == 0Tier 1 Candidate (LLM Prompt Gen):
MUT_01:temp == 280(Testing extreme load)MUT_02:power_draw > 50(Asserting full subsystem power)MUT_03:voltage == 24(Unrelated stable parameter)
Execution Trace
axioms = [
Axiom(
"AX_01",
"z3.Implies(temp > 250, circuit_open == True)",
"Hardware thermal cutoff invariant",
),
Axiom(
"AX_02",
"z3.Implies(circuit_open == True, power_draw == 0)",
"Power bus physical disconnection rule",
),
]
mutables = [
MutableConstraint(
"MUT_01", "temp == 280", "High-temperature operating test point"
),
MutableConstraint("MUT_02", "power_draw > 50", "Full system draw target"),
MutableConstraint("MUT_03", "voltage == 24", "Nominal bus voltage"),
]
engine = TieredVerificationEngine(
variables={
"temp": "Real",
"circuit_open": "Bool",
"power_draw": "Real",
"voltage": "Real",
},
axioms=axioms,
)
status, culprits, msg = engine.verify_and_isolate(mutables)
Solver Outcome:
solver.check()returnsz3.unsat.solver.unsat_core()returns:[track_MUT_01, track_MUT_02].Notice that
AX_01andAX_02are not present in the core, despite being the exact logical clauses that bridgetemp == 280topower_draw == 0.MUT_03is cleanly omitted because it is orthogonal to the conflict.
4. Framing the Isolated Core for the LLM
To guarantee the LLM does not try to mutate the environment or touch unaffected rules, format the feedback using Scoped Action Boundaries:
def format_scoped_feedback(
axioms: List[Axiom],
culprits: List[MutableConstraint],
all_mutables: List[MutableConstraint],
) -> str:
culprit_ids = {c.id for c in culprits}
untouched_mutables = [m for m in all_mutables if m.id not in culprit_ids]
prompt = [
"### SMT VERIFICATION REPORT: MUTABLE SET CONTRADICTION",
(
"Your generated parameters violate immutable system axioms."
" Mathematical conflict isolated."
),
"",
"#### IMMUTABLE SYSTEM AXIOMS (READ-ONLY — CANNOT BE MODIFIED):",
]
for ax in axioms:
prompt.append(
f"- [{ax.id}]: `{ax.expression}` (Ground Truth: {ax.description})"
)
prompt.extend([
"",
"#### CONFLICTING PROPOSED CONSTRAINTS (MUTATION REQUIRED):",
(
"The solver proved that the following proposed constraints cannot"
" simultaneously coexist with the axioms:"
),
])
for c in culprits:
prompt.append(
f"- [{c.id}]: `{c.expression}` (Source Ref: {c.requirement_source})"
)
prompt.extend([
"",
f"#### LOCKED CONSTRAINTS (DO NOT MODIFY):",
f"The following {len(untouched_mutables)} constraints passed verification and must remain unchanged:",
", ".join(f"`{m.id}`" for m in untouched_mutables)
if untouched_mutables
else "None",
"",
"#### INSTRUCTIONS:",
(
f"1. Modify ONLY the constraints in the conflicting set: {list(culprit_ids)}."
),
(
"2. Adapt your parameters so they logically adhere to the immutable"
" axioms."
),
"3. Output the updated constraint set conforming to the JSON schema.",
])
return "\n".join(prompt)
5. Architectural Guardrails for Hierarchy Integrity
Static Name Isolation: Prevent namespace collisions. Prefix all internal axiom variables and solver tracker symbols (
ax__*,track__*) so the LLM cannot inject assignments to them inside its AST expression payload.Pre-Check Axiom Consistency: At orchestrator boot, evaluate the Tier 0 axioms in isolation:
base_solver = z3.Solver() # Add all axioms... if base_solver.check() != z3.sat: raise RuntimeError("CRITICAL: System axioms are inherently UNSAT!")If the base axioms are contradictory on their own, the LLM will never be able to produce a satisfiable mutable set, leading to an infinite retry loop.
Solver Push/Pop for High Throughput: Instead of rebuilding the solver context on every LLM retry pass, add axioms to the root context, invoke
solver.push(), test the tracked mutables, and runsolver.pop()on failure. This retains the internalized pre-computed lemmas for the axioms across iterations, cutting latency significantly.
From <https://gemini.google.com/app/3ec7c172cbe0ba89> Google Gemini (3.8 Flash)
Comments
Post a Comment